US5640436A  Method and apparatus for Xray computed tomography  Google Patents
Method and apparatus for Xray computed tomography Download PDFInfo
 Publication number
 US5640436A US5640436A US08/588,918 US58891896A US5640436A US 5640436 A US5640436 A US 5640436A US 58891896 A US58891896 A US 58891896A US 5640436 A US5640436 A US 5640436A
 Authority
 US
 United States
 Prior art keywords
 correction table
 projection data
 means
 ray
 projection
 Prior art date
 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
 Expired  Lifetime
Links
 238000002591 computed tomography Methods 0 abstract claims description title 71
 238000009826 distribution Methods 0 abstract claims description 24
 230000000694 effects Effects 0 abstract claims description 13
 238000000034 methods Methods 0 abstract description 44
 230000014509 gene expression Effects 0 claims description 26
 239000010950 nickel Substances 0 claims description 14
 238000004364 calculation methods Methods 0 claims description 13
 230000000875 corresponding Effects 0 claims description 4
 239000002004 ayurvedic oil Substances 0 claims 5
 239000011799 hole materials Substances 0 description 30
 238000003384 imaging method Methods 0 description 17
 230000015654 memory Effects 0 description 9
 239000010933 palladium Substances 0 description 8
 238000001914 filtration Methods 0 description 5
 238000007781 preprocessing Methods 0 description 5
 238000003860 storage Methods 0 description 4
 230000001131 transforming Effects 0 description 4
 241000226585 Antennaria plantaginifolia Species 0 description 3
 238000009828 nonuniform distribution Methods 0 description 3
 230000035945 sensitivity Effects 0 description 3
 238000003745 diagnosis Methods 0 description 2
 230000001965 increased Effects 0 description 2
 239000002184 metal Substances 0 description 2
 229910052751 metals Inorganic materials 0 description 2
 229910052759 nickel Inorganic materials 0 description 2
 230000001603 reducing Effects 0 description 2
 238000009877 rendering Methods 0 description 2
 210000001015 Abdomen Anatomy 0 description 1
 210000004197 Pelvis Anatomy 0 description 1
 229910052770 Uranium Inorganic materials 0 description 1
 230000002159 abnormal effects Effects 0 description 1
 238000002583 angiography Methods 0 description 1
 238000004422 calculation algorithm Methods 0 description 1
 239000002131 composite material Substances 0 description 1
 239000004567 concrete Substances 0 description 1
 238000007796 conventional methods Methods 0 description 1
 238000002059 diagnostic imaging Methods 0 description 1
 238000000605 extraction Methods 0 description 1
 230000002093 peripheral Effects 0 description 1
 230000001629 suppression Effects 0 description 1
 238000003786 synthesis Methods 0 description 1
 229910052720 vanadium Inorganic materials 0 description 1
Images
Classifications

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
 G06T11/00—2D [Two Dimensional] image generation
 G06T11/003—Reconstruction from projections, e.g. tomography
 G06T11/005—Specific preprocessing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating

 A—HUMAN NECESSITIES
 A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
 A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
 A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
 A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
 A61B6/5258—Devices using data or image processing specially adapted for radiation diagnosis involving detection or reduction of artifacts or noise

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
 Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y10S378/00—Xray or gamma ray systems or devices
 Y10S378/901—Computer tomography program or processor
Abstract
Description
The present invention relates to a method and an apparatus for conebeam Xray computed tomography that include an Xray source for applying a conebeam X ray, a twodimensional Xray detector for detecting the X ray and a scanner having the detector mounted thereon so that the scanner is rotated around an object to be inspected (hereafter simply called an object) for measuring the data projected from multiple points located around the object and reconstructing a distribution of Xray attenuation coefficients of the object based on the measured data. FIG. 2 shows a common arrangement of the conventional conebeam Xray computed tomography. The conventional conebeam Xray computed tomography is arranged to have a measuring unit 1, a data processing unit 2 for processing the measured data, and a unit 18 for preparing geometric distortion correction table. On a scan mechanism 3, an Xray source 4 is located as opposed to a twodimensional detector 5 such as an Xray image intensifier (I. I.) with an object 6 laid midway between the Xray source 4 and the detector 5. The Xray source 4 operates to apply a conebeam X ray 7 to the object 6. The twodimensional detector 5 operates to detect an intensity of the X ray passed through the object 6. The scan mechanism 3 is measuring the intensity of the X ray passed through the object with the detector 5 as it is rotating around the object 6.
The measured data is digitized and then is sent to the data processing unit 2. The data processing unit 2 operates to correct adverse effects applied on the measured data by a dark current bias and a nonuniform distribution of sensitivity of the detector and logarithmictransform the corrected data into the projection data in a preprocessing unit 9. This logarithmic transformation is the same as the transformation performed by the conventional fanbeam Xray computed tomography. Next, a geometric distortion correction unit 10 operates to correct the geometric distortion using the geometric distortion correction table. The geometric distortion basically depends on the characteristic of the twodimensional detector 5. Of the candidate detectors, the image intensifier may bring about a pincushion distortion far remarkably than any other detector. The occurrence of the pin cushion distortion results from the spherical input surface of the image intensifier. If the projection data contains the geometric distortion, the data does not allow the precise reconstruction of the distribution of the Xray attenuation coefficients. Hence, the correction for the image geometric distortion is an indispensable preprocessing step. In place, the correction for the image geometric distortion may be executed for the measured data before correcting the adverse effects caused by the dark current bias and the nonuniform distribution of sensitivity of the detector or performing the algorithmic transformation. As an example of the aforementioned geometric distortion correcting method, please refer to the writing: R. Ning, et al. "An Image Intensifierbased Volume Tomographic Angiography Imaging System: Geometric Distortion Correction, (SPIE Vol.2163, Physics of Medical Imaging, pp.199210, 1994) or the Japanese unexamined laidopen No. Hei 528316 (JPA528316).
On the overall projection data formed by the foregoing preprocessing, the image reconstructor 11 operates to reconstruct a threedimensional distribution of attenuation coefficients of the object 6 within a field of view. This threedimensional reconstructed data is subject to a volume rendering process or an imaging process like a maximum intensity projection process in the rendering unit 12. As this kind of reconstructing operation, a Feldkamp's conebeam reconstructing operation is known. (see the publication: L. A. Feldkamp et al., "Practical Conebeam Algorithm", J. Opt. Soc. Am. A, Vol.1, No.6, pp. 612619, 1984).
Of the foregoing processes, the correction for geometric distortion will be discussed in detail. If the image intensifier is used as the twodimensional detector 5, the typical geometric distortion appears as shown in FIG. 6. The image shown in FIG. 6 is obtained by fixing to the front of the detector (image intensifier) 5 a metal plate having pin holes opened at regularly spaced lattice points as shown in FIG. 7 and imaging a projected image. Such a metal plate is called a hole chart 19. The projected image picked by the above process is called a hole chart projected image. This geometric distortion results from a composite of some causes. The first cause is a geometric distortion of the measuring system. The second cause is a nonplanar input surface of the detector 5. In particular, if the detector 5 uses the image intensifier, the spherical input surface of the image intensifier brings about the pincushion distortion in the projection data. The third cause is a deviation of an electron beam of an electronic camera system contained in the image intensifier, the deviation being caused by an external magnetic field such as geomagnetism.
As a method for correcting an image geometric distortion, it is possible to refer to the correcting method built in the technique known as a realtime DR (Digital Radiograph) that uses the image intensifier as the detector 5. The correction for the image geometric distortion is executed by referring to the geometric distortion correction table. This table is saved in the memory 20 for saving geometric distortion correction table. Later, the description will be oriented to the conventional image geometric distortion correcting method arranged to refer to the correction table. The projected image (containing the image geometric distortion) is represented as Pd(U, V). The projected image from which the image geometric distortion is removed is represented as P(u, v). Between both of the images, the relation of P(u, v)=Pd (U(u,v), V(u, v)) is set up, in which the functions U (u, v) and V (u, v) represent the image geometric distortion. In actual, the variables u and v assume discrete values. Hence, these functions U and V are put into a table, then the image geometric distortion is corrected by looking up the table. This table corresponds to a geometric distortion correction table. More particularly, the image geometric distortion correction is executed by preparing the table U (u, v) and V (u, v) in which (u, v) are assumed as pixel coordinates like u=1, 2, . . . , N_{i} and v=1, 2, . . . , N_{j} and deriving P (u, v) by the fourpoint Lagrange's interpolation as indicated in the expression (1). ##EQU1## where U and V are the maximum integers that do not surpass U(u, v) and V(u, v), respectively.
The geometric distortion correction table U(u, v) and V(u, v) is generated by using the foregoing hole chart projected image in the geometric distortion correction table generator 21. The generation of the correction table will be executed as follows. The prepared geometric distortion correction table is saved in the memory 20 for geometric distortion correction table. At first, the pin hole locations are extracted from the hole chart projected image. For the extraction, a proper image recognition technique is used. In the hole chart projected image, as shown in FIG. 7, a pin hole at the ith dot in the u direction and the jth dot in the v direction is represented as h_{ij}, the location of which is represented as (u_{ij}, v_{ij}). In actual, as shown in FIG. 7, the pin holes are not allowed to be at the respective lattice points. For the explanation's sake of convenience, it is assumed that the pin holes are located at the respective lattice points. In the projected image containing the image geometric distortion, the pin hole for the pin hole h_{ij} is H_{ij} and is located at (U_{ij}, V_{ij}). Through the effect of the foregoing functions U(u, v) and V(u, v), the image geometric distortion correction table at this lattice point, that is, the pin hole location is represented as U_{ij} =U(u_{ij}, v_{ij}) and V_{ij} =V(u_{ij}, v_{ij}). The other points except this lattice points (pin hole locations) are defined by the fourpoint Lagrange's interpolation as indicated in the expression (2). Assuming that u=1, 2, . . . , N_{i} and v=1, 2, . . . , N_{j}, the geometric distortion correction table values U(u, v) on all the points (u, v) are defined. The similar operation is performed with respect to V(u, v). ##EQU2## wherein U and V are maximum integers that do not surpass U_{ij} and V_{ij}, respectively.
In the conebeam Xray computed tomography apparatus, to correct the image geometric distortion, the foregoing geometric distortion correction is executed for the overall projection. In addition, the geometric distortion correction table is generated at least once when the conebeam Xray computed tomography apparatus is installed and then at a routine maintenance time.
The foregoing conventional conebeam Xray computed tomography apparatus has two shortcomings. One of them is a small field of view of the twodimensional Xray detector (simply called a detector), and the other one is a requirement for a massive amount of data for preparing a geometric distortion correction table.
At first, the description will be oriented to the shortcoming resulting from the small field of view of the detector.
In the foregoing conventional apparatus, the field of view of the detector 5 is not large enough to cover the overall object 6. For example, consider that the detector 5 uses the image intensifier. The image intensifier does not have so long a diameter of a plane of incidence. Concretely, it is about ten and some inches (about 30 cm). The object reflected in the field of omnidirectional projections is basically a small spherical region whose diameter is as short as about 20 cm, though the actual region depends on the geometry of the overall measuring system. This restriction of the field of the detector 5 truncates the peripheral portion of the object from the projection data. The projection data with the portion truncated from the actual object is applied to the foregoing Feldkamp's conebeam reconstructing operation for reconstructing the threedimensional reconstructed data. In this threedimensional data, the Xray attenuation coefficients around the region boundary of the field are made higher than the actual attenuation coefficients, while the attenuation coefficients in the central region of the field are made lower. That is, a shading artifact phenomenon takes place. This reconstructed data does not exactly represent the distribution of the Xray attenuation coefficients of the object 6, on which data no right diagnosis is allowed. One of the methods for avoiding this shortcoming is that the field of the detector 5 is made large enough to cover the overall object 6. In actual, however, it is difficult to realize the detector 5 with so large a field. The region of interest substantially required for the actual diagnosis is not so large. Hence, if the detector 5 may has a large field of view, the resulting Xray computed tomography apparatus is made so large in scale and needs a larger amount of calculations. Such an apparatus is not realistic.
In order to avoid occurrence of the shading artifact phenomenon, therefore, it is necessary to realize the method for reconstructing an exact distribution of Xray attenuation coefficients in the region of interest from the incomplete projection data, that is, the data that does not contain the data about the portion of the object departed from the field of the detector. To realize this kind of method, various studies have been tried. (For example, refer to the writing: R. M. Lewitt., Processing of Incomplete Measurement Data in Computed Tomography, Medical Physics, Vol.6, No.5, pp.412417, 1979.) The Lewitt's truncation correction is a method for compensating for the data about the portion cut out of the projection data through the effect of the extrapolation. The extrapolation needs information about a rough contour of the object. The contour is assumed as a relatively simple form like an ellipse or measured by a sensor. However, the truncation correction used in the fanbeam Xray computed tomography is not straightforward applicable to the conebeam Xray computed tomography to which the present invention applies. The causes therefor are as follows.
The first cause is a curved surface of incidence of the image intensifier. Further, the influence of geomagnetism does not stabilize the boundary of the field of the detector 5 on the projection data. It is hence difficult to distinguish a site of the object located within the field of the detector 5, that is, the site about which the exact data is measured, from another site of the object outside the field, that is, the site about which the inexact data is measured. As mentioned above, the conebeam Xray computed tomography apparatus supplies the projection data after temporarily correcting the geometric distortion of the measured data obtained by the detector 5. The projection data that was subject to the geometric distortion correction does not correspond to the measured data at a onetoone ratio. Further, the image geometric distortion indicates a pattern varied at each projection (angle). Therefore, on the projection data whose geometric distortion is corrected, the boundary of the field is made variable at projections (angles). On the other hand, the fanbeam Xray computed tomography apparatus supplies the projection data that corresponds to the data measured by the Xray detector array at a onetoone ratio. Therefore, the boundary of the field on the projection data straightforward corresponds to the both ends of the Xray detector array. It means that the fanbeam Xray computed tomography apparatus does not have the foregoing shortcoming.
The second cause is difficulty in presuming or measuring the contour of the object. The region of interest to be imaged covers various sites of the object such as a head, a thorx, an abdomen and a pelvis, and each object is different. Hence, it is not easy to presume the contour of the object. Several patterns of the contours may be prepared for allowing the most approximate one to the region of interest of the object to be selected. In this case, the data on which the pattern is selected has to be read by an operator' manual operation, a sensor or the like. Besides, the measurement of the contour for each object makes the overall apparatus idly complicated, and it is not realistic.
The detector 5 like the image intensifier may selectively have a field of view in imaging the object. For a small region of interest, the field of view may be adjusted to be smaller for improving spatial resolution. It is hence desirable to select the truncation correction method, because this method may easily cope with change of a field size if the detector 5 is required to switch the field size when imaging the object.
Next, the description will be oriented to the second shortcoming about a geometric distortion correction table. As mentioned above, the conebeam Xray computed tomography apparatus may use the geometric distortion correction method built in the realtime DR technique for the geometric distortion correction for the measured data. In actual, however, the conebeam Xray computed tomography needs to process a massive amount of data. Hence, the straightforward application of this correction method may bring about the shortcoming. The first cause (geometric distortion of the measuring system) and the second cause (nonplanar measuring surface of the detector 5), which have been described above as the causes for bringing about the image geometric distortion, do not depend on the direction of the detector 5, that is, the projection angle. However, the image geometric pattern based on the third cause (variable deviation of an electron beam of the electron camera system provided in the image intensifier) varies at each projection angle. It indicates that this correction method needs to prepare the geometric distortion correction tables for all the projection angles. That is, the hole chart projection image is required to be picked at every projection angle. To execute it, however, the following problem rises.
In general, the conebeam Xray computed tomography needs to take a hundred to several hundred of images. The conventional fanbeam Xray computed tomography has already provided the image reconstruction based on more than hundreds of projection images. The conebeam Xray computed tomography is often required to process such a number of images. In any case, the conebeam Xray computed tomography needs to take such many images and process a massive amount of data. Hence, a certain kind of data abnormality probably takes place in the processing of a signal at the measuring time, transmission of a signal, internal or external storage means or data transfer between storage means (memories). If the data abnormality appears on the hole chart projection image. the abnormality stops to generate the exact geometric distortion correction table. For example, inclusion of noise causes a pin hole to be erroneously recognized at a spot where no pin hole is located. Even one erroneously recognized pin hole may greatly impair the geometric distortion correction table generated on the pin hole. If the geometric distortion correction table is not exactly generated, no exact geometric distortion of the projection data is expected. Hence, the threedimensional image is not exactly reconstructed.
As means for solving the foregoing problem, improvements for suppressing the abnormality on the image data are added to the measuring system, the signal processing system, the signal transmitting system, the information storage means, and the like. However, these improvements needs to overall or partially redesign the apparatus. This results in making the apparatus more costly. Further, however sophisticated the prepared hardware may be, complete avoidance of abnormal data is impossible.
As means for solving the problem, hence, it is desirable to check if the geometric distortion correction table is exactly generated and replace the table with missing data with a right one at the postprocessing step.
It is a first object of the present invention to provide a method and an apparatus for conebeam Xray computed tomography that are arranged to overcome the foregoing first shortcoming the prior art involves and realize the processes (1) of detecting the field boundary on the projection data at every projection angle and (2) of stably treating data without having to depend on the contour and the size of the object 6 and the field size of the detector 5 when imaging the object as a truncation correction method.
In a conebeam Xray computed tomography method of reconstructing a distribution of Xray attenuation coefficients of the object by using projection data measured in the omnidirectional projections of the object by rotating a scanner mounting an Xray source for applying a conebeam X ray and a twodimensional Xray detector for detecting the Xray, the first object of the invention is achieved by an Xray computed tomography method including the steps of correcting an image geometric distortion of the projection data, detecting a field boundary from the projection data whose geometric distortion is corrected, generating the projection data of a certain width from the detected field boundary through the effect of an extrapolating calculation, and reconstructing the distribution of Xray attenuation coefficients of the object from the extrapolated projection data.
Further, the first object of the invention is achieved by an Xray computed tomography apparatus arranged to have a scanner mounting an Xray source for applying a conebeam X ray and a twodimensional Xray detector and rotate the Xray source and the scanner around an object for measuring the data of the omnidirectional projections around the object and reconstructing the distribution of the Xray attenuation coefficients of the object based on the projection data, which apparatus includes a unit for correcting an image geometric distortion of the projection data, a unit for detecting a field boundary of the projection data whose geometric distortion is corrected by the correcting unit, an extrapolating unit for generating the projection data of a certain width from the detected field boundary, and a unit for reconstructing the distribution of the Xray attenuation coefficients of the object from the projection data extrapolated by the extrapolating unit.
It is a second object of the present invention to provide a method and an apparatus for Xray computed tomography which are arranged to overcome the foregoing second shortcoming the prior art involves and to generate the exact geometric distortion correction tables at every projection angle without having to improve the apparatus itself.
The second object of the present invention is achieved by a method for conebeam Xray computed tomography, which includes in the step of generating a geometric distortion correction table for correcting a peculiar geometric distortion to the detector a step of generating a geometric distortion table, a step of temporarily saving the geometric distortion table, a step of calculating an evaluation function for evaluating the geometric distortion table, a step of comparing a calculated value of the evaluation function with a predetermined threshold value, and a step of generating a geometric distortion correction table through the effect of the interpolation.
Further, the second object of the present invention is achieved by an apparatus for conebeam Xray computed tomography, which includes in a unit for preparing a geometric distortion correction table for correcting a peculiar geometric distortion to a twodimensional detector a unit for generating a geometric distortion correction table, a unit for temporarily saving the geometric distortion correction table, a unit for calculating an evaluation function for evaluating the geometric distortion correction table, a unit for comparing the calculated value of the evaluation function with a predetermined threshold value, and an interpolating unit for preparing the geometric distortion correction table through the effect of the interpolation.
The method and apparatus for Xray computed tomography according to the present invention are arranged to compensate the projection data for a truncated projection of an object through the effect of the extrapolation. In order to meet the aforementioned two conditions required by the truncation correction method used in the conebeam Xray computed tomography, the method and the apparatus according to the invention include the steps of (1) setting zero or a special value for the out of a view field to a region of the projection data for the out of a view field of the detector 5, (2) detecting a location of a view field boundary by scanning the projection data, and (3) extrapolating the projection data of a certain width from the detected location of the view field boundary to the outoffield region.
Along these processes, the method and the apparatus according to the present invention enable to avoid the artifact phenomenon on the reconstructed data resulting from the portion of the object truncated from the view field of the detector without having to add further components like a sensor when reconstructing the image or calculate a large amount of load.
That is, the method and the apparatus according to the present invention enable to avoid the artifact phenomenon without having to add further instruments or operations under variable imaging conditions in which the region of interest and the location of the object 6 is variable, each object is different, the field size of the detector 5 is variable in imaging the object, and the like.
Moreover, the method and the apparatus according to the present invention operate to check the geometric correction table and reprepare the exact geometric correction table, as indicated below, for solving the foregoing problem that the geometric distortion correction table is not exactly generated.
The geometric distortion correction table is generated from the hole chart projection image at every projection angle. The table is generated at each projection angle according to the foregoing geometric distortion correction table preparing method. The generated geometric distortion correction table is checked. Concretely, the geometric distortion correction table of the object projection angle of interest is compared with the geometric distortion correction tables before and after the correction table of interest. If the discontinuous change between the correction tables is found, the correction table is removed. In place of the removed correction table, a new correction table is interpolated from the correction tables before and after the removed table and then is replaced with the removed table.
According to the present invention, as such, the exact geometric distortion correction table at every projection angle may be obtained by a simple method without having to redesign all or part of the apparatus.
At first, the conventional geometric distortion correction table preparing process is applied to the hole chart projection image at every projection angle for obtaining the geometric distortion correction table at every projection angle. Then, the continuity between the correction tables at the adjacent projection angles is checked for determining if the correction table is exactly prepared. If it is not, in place of the inexact correction table, a new geometric distortion correction table is interpolated from the correction tables before and after the inexact correction table and then is replaced with the inexact one. After these series of processes, the exact geometric distortion tables at all projection angles are prepared.
As set forth above, the method and the apparatus for Xray computed tomography according to the present invention realizes as the truncation correction method for the conebeam Xray computed tomography the processes of (a) sensing a location of a view field boundary on the projection data at each projection and (b) keeping the correction independent of the contour form and the size of the object and the size of an imaging field of the detector. More particularly, the method and the apparatus enable to avoid the artifact phenomenon on the reconstructed data resulting from the portion of the object truncated out of the view field of the detector. The method and the apparatus according to the invention perform an extrapolation about the imaged projection data for compensating the projection data for the truncated portion. This makes it possible to avoid the artifact phenomenon. In particular, the characteristic effects of the present invention are as follows: (1) The avoidance of the artifact phenomenon is made possible without having to add special instruments and operations even under variable imaging conditions, that is, if the location of the region of interest to be imaged, the object to be checked, and the size of the imaging field of the detector are variable at each projection. (2) The exact geometric distortion correction table can be obtained by a simple method at every projection angles without having to redesign the whole or the part of the apparatus.
Briefly, according to the invention, the apparatus for conebeam Xray computed tomography operates to correct an image geometric distortion of the projection data, detect the location of the field boundary of the distortioncorrected projection data, generate the projection data of a certain width from the detected location of the view field boundary through the effect of the extrapolation, and then reconstruct a distribution of Xray attenuation coefficients from the extrapolated projection data. To prepare the exact geometric distortion correction table, the apparatus operates to apply the conventional process of preparing a geometric distortion correction table to the hole chart projection images of all the projection angles for preparing the geometric distortion correction table of all the projection angles, check the continuity between the adjacent tables for checking the correction table for determining whether or not it is inexact, prepare a new table through the interpolation of the previous and the successive correction tables, if any, and replace the inexact table with the new table. These operations result in realizing the exact geometric distortion correction table for all the projection angles.
FIG. 1 is a block diagram showing an arrangement of an apparatus for conebeam Xray computed tomography according to an embodiment of the present invention;
FIG. 2 is a block diagram showing an arrangement of the conventional apparatus for conebeam Xray computed tomography;
FIG. 3 is an explanatory view showing a geometric relation of a measuring unit provided in the apparatus for conebeam Xray computed tomography according to the invention;
FIG. 4 is a view showing a profile of projection data with a truncated portion of an object to be imaged;
FIG. 5 is a view showing a profile of projection data extrapolated by the apparatus of the invention;
FIG. 6 is a view showing locations of pin hole images on a hole chart projection image with an image geometric distortion output by the prior art;
FIG. 7 is a view showing location of pin holes on a hole chart projection image used in the prior art;
FIG. 8 is a view showing a detailed arrangement of a unit for preparing a geometric distortion correction table according to the present invention; and
FIG. 9 is a view showing a change of an evaluation function valU(a) used in the present invention.
Later, the description will be oriented to an embodiment of the invention with reference to the drawings. FIG. 1 is a block diagram showing an arrangement of an apparatus for conebeam Xray computed tomography. For further discussion, the geometric relation of a measuring system will be shown in FIG. 3. In FIG. 3, a threedimensional coordinate system (x, y, z) is the Cartesian coordinates secured to the object, in which a z axis is a center axis 8 of rotation. An xray source 4 and a scanner 3 with a twodimensional Xray detector 5 (simply called a detector) installed thereon are rotated on the z axis. The angle of rotation of the scanner 3 is denoted by w. A twodimensional coordinate system (u, v) is the Cartesian coordinates secured to the detector 5, in which a v axis is parallel to the z axis and a u axis is parallel to the tangent of the rotation. At first, like the prior art, the measured data obtained by imaging the object is preprocessed for converting it to the projection data in a preprocessing unit 9. The preprocesses contain a dark current bias correction, a correction for a nonuniform distribution of sensitivity of the detector, and a logarithm transformation. This projection data is saved in a first memory (not shown) and is represented by Pd(m, n, k). Assuming that m, n and k are an index for the u direction, an index for the v direction, and an index for the rotation angle w of the scanner 3, respectively, and N_{m}, N_{n} and N_{k} are a number of udirectional data pieces, a number of vdirectional data pieces, and a number of projections, respectively, the possible ranges of m, n and k are {1, 2, . . . , N_{m} }, {1, 2, . . . , N_{n} } and {1, 2, . . . , N_{k} }, respectively.
Next, the image geometric distortion is corrected in a geometric distortion correction unit 10. The correction is executed on a geometric distortion correction table, the preparation of which will be described below. This distortioncorrected projection data is saved in a second memory (not shown), in which the data is represented as P(i, j, k). Assuming that i, j and k are an index for the u direction, an index for the v direction, and an index for an angle of rotation w of the scanner 3, respectively and N, Nj and Nk are a number of udirectional data pieces, a number of vdirectional data pieces, and a number of projections, respectively, the possible ranges of i, j and k are {1, 2, . . . , N_{i} }, {1, 2, . . . , N_{j} } and {1, 2, . . . , N_{k} }, respectively. At a time, between the distortioncorrected projection data and the projection data before the correction, the relation of P(i, j, k)=Pd(M(i, j, k), N(i, j, k), k) is set up. M(i, j, k) and N(i, j, k) are functions about i, j and k derived according to how much the image is geometrically distorted. These functions do not normally yield integer values. Hence, P(i, j, k) are interpolated from Pd(M(i, j, k), N(i, j, k), k) through the effect of a technique such as a linear interpolation. Since the region located out of the view field of the detector 5 is determined on the projection data before correcting the distortion, if Pd(M(i, j, k), M(i, j, k), k) stays out of the view field, zero is substituted in P(i, j, k).
Next, upon the projection data obtained after correction of geometric distortion, a truncation correction for the status that the projection data is out of view field is performed in truncation correction unit 15. The truncation correction unit 15 includes view field boundary detecting unit 16 and extrapolation calculation unit 17. The truncation correction processing includes a processing (1) for detecting a view field boundary in the view field boundary detecting unit 16 and an extrapolation processing (2) in the extrapolation calculation unit 17.
At first, (1) detection of a view field boundary will be described. As mentioned above, after the image distortion is corrected, the form of the view field boundary of the detector 5 is variable according to each projection. Hence, the process of detecting the field boundary is executed for detecting the field boundary of the detector for each projection. Herein, the distortioncorrected projection data P(i, j_{0}, k_{0}) in which {i=1, 2, . . . , N_{i} } is assumed for any j_{0} and k_{0} is called a scan line. The field boundary is detected by scanning the distortioncorrected projection data P(i, j, k) at each scan line in the i direction. Based on a certain scan line, the view field boundary with a smaller value of i is called a left end, while the field boundary with a larger value of i is called a right end. To specify the left end of the field, as incrementing a value of i from 1, the scan line is being checked until a value other than zero is found on the scan line. If such a value is found, that is the left end of the view field on the scan line. If such a value is not found until i=N_{i}, it indicates that the overall scan line is out of the view field of the detector 5. If the left end is found, then, the right end of the view field on the scan line is detected. That is, as decrementing a value of i from N_{i}, the scan line is being checked until a value other than zero is found. If the overall scan line is not located out of the view field, the abovedescribed operations are realized to detect the left and the right ends of the view field, that is, the view field boundary.
In some relation between the view field and the object 6, the projection data may have zero if the scan line stays within the view field of the detector. If a value of zero is used for indicating out of the field, in such a relation, the exact field boundary may not be detected. In that situation, the detected field boundary actually indicates the contour of the object. However, this detected field boundary may be assumed as a temporary field boundary for executing the subsequent extrapolation. In the foregoing description, zero is saved in portions out of the view field when correcting an image geometric distortion. In actual, however, any special value may be used if it is distinguishable from the projection value inside the view field. In the latter case, when extracting the field boundary, the scan line is being scanned until any value other than the value indicating the out of field is reached.
Next, (2) the extrapolation processing will be described. FIG. 4 shows a profile of one scan line of a typical projection data with a missing portion, because a portion of the object is out of the field of view. The left end of the field of the overall scan line found when detecting the field boundary is assumed to be i=a and the right end thereof is assumed to be i=b. If the overall scan line stays out of the field, no processing is executed for this scan line, and then the processing is transferred to the next scan line. c denotes a crosspoint between this scan line and a straight line formed by projecting the center axis of rotation x=y=0 on the uv plane or a point derived by the expression of c=(a+b)/2.
As shown in FIG. 4, the section [a, b] indicates the field. The projection data out of the field is missing. The projection data is discontinuously changed at the field ends of i=a, b.
The foregoing Feldkamp's conebeam reconstructing operation is executed so that the SheppLogam function or its analogous correction filter is acted for the projection data. The correction filter serves to emphasize a high frequency component of the projection data, so that a spikelike peak appears on the field ends of the projection data, that is, at the points of i=a, b. The back projection of the filtered projection data results in bringing about a shading artifact on the reconstructed data. To avoid the artifact, the apparatus of this embodiment operates to do an extrapolating process in the portion out of view field for generating continuous projection data (1) continued with the projection data which is present in the view field at the field ends of i=a, b and projection data (2) taking zero at points separated by a certain width from the field ends of i=a, b. The filtering process executed in the conebeam reconstructing operation is proceeded only in the i direction. Hence, this extrapolating process is just required to consider only the idirectional continuity. The later discussion concerns with only the idirectional extrapolation.
Later, the description will be oriented to a concrete extrapolating method meeting the foregoing conditions of this embodiment. At first, the method for creating extrapolation data on the side of i<a is described. As shown in FIG. 5, the extrapolated curve is made to be (1) an ellipse with a straight line i=c and an i axis as its axes and pass through (2) a point (a, P(a, j, k)) and (3) a point (a', 0). If the extrapolating width is assumed as D, the value of a' is defined as a'=aD. The ellipse meeting the condition (1) is represented by the expression (3); ##EQU3## wherein A is a major axis and B is a minor axis.
The values of A and B are determined on the expression (4) so that these A and B meet the conditions (2) and (3). ##EQU4##
In turn, the extrapolation of a section (a', a] is derived by the expression (5); ##EQU5##
On the other hand, about the section (b, b'], the variables of a and a' are replaced with b and b' (where b'=b+D). That is, the foregoing extrapolation will be summarized below. If on the projection plane of the detector the u axis is located in the direction of rotation of the scanner and the v axis is located in parallel to the center axis of rotation of the scanner, a projection value at the kth projection location (u, v) is P(u, v, k), the boundary of the field detected about a certain value of v is (a(v), v) and (b(v), v) (where a(v)<b(v)). And, if the projection of the center axis of rotation is a straight line u=c and the certain width to be extrapolated is D, the projection data out of the view field is extrapolated according to the expression (6) in the region out of the view field u<a(v) and b(v)<u. ##EQU6##
The foregoing preprocess, geometric distortion correction, and extrapolation may be executed at a batch after all the projection data is obtained or at each projection. The Feldkamp's conebeam reconstructing operation, however, may perform a reconstructing operation for each projection. Hence, the method for performing the preprocess, the geometric distortion correction, and the extrapolation for each projection is superior in that the operations (the preprocessing, the geometric distortion correction, the extrapolation and the reconstructing operation) are done concurrently with imaging an object.
Further, the preprocess, the geometric distortion correction, the extrapolation and the reconstructing operation are executed for each scan line. Hence, these processes may be performed in that order at each scan line. This method is effective in reducing in size the necessary volume of the storage means for temporarily saving the extrapolated projection data. In place of doing these processes for each projection or scan line in turn, the units for doing these processes are multiplexed so that these processes may be done concurrently for a group of some projections or scan lines.
As set forth above, this method allows the values of a' and b' and the width of the extrapolated projection data to be optionally determined before imaging the object. This is advantageous in that fast calculations are made possible when doing a filtering process for the reconstructing operation. As mentioned above, the conebeam reconstruction operation is executed to apply the SheppLogan function or its analogous correction filter to the projection data in the operating process. In general, this filtering process uses the fast Fourier transform for reducing an amount of calculations. As well known in the art, the fast Fourier transform displays the most excellent performance when the number of data pieces is a power of 2. For example, if the number of data pieces to be input is 480, the number is made to be 512 by padding the zero values before doing the fast Fourier transform.
On the contrary, if the number of data pieces is 513, for example, the number has to be increased to 1024. This results in extremely increasing the amount of calculations. For example, assume that the used extrapolating method is arranged to define the width of the extrapolated projection data based on the information about the contour of the object 6. In this case, the number of data pieces does not necessarily reach the most approximate number for the fast Fourier transform. This method may, hence, bring about increase of an amount of calculations. However, the foregoing extrapolating method according to the present invention allows the width of the extrapolated projection data to be set to a suitable value for the filtering process.
This is quite advantageous because the reconstructing operation done in the conebeam Xray computed tomography needs a massive amount of filtering processes. Of course, the foregoing extrapolation may use a higher degree function. The complexity of the function is determined on the required accuracy, the time of calculation, the cost taken in installing the calculating unit, and the cost taken in manufacturing the apparatus. Further, the conebeam Xray computed tomography apparatus may be arranged so that the view field of the detector 5 changes according to a selected one of imaging modes. However, the method of this invention may perform the same processing even if the imaging condition is changed.
Moreover, the method of the invention is not influenced by the location of the region of interest to be imaged and each feature of the object 6. This is quite advantageous in that the method may apply to various imaging conditions without changing the apparatus scale and the amount of calculations. According to the foregoing embodiment, the artifact phenomenon on the reconstruction data may be avoided without calculating a large amount of extra information and load in reconstructing the image.
In turn, the description will be oriented to the embodiment of the invention for overcoming the second shortcoming, that is, the shortcoming about a method for preparing the geometric distortion correction table used in the conebeam Xray computed tomography. At first, a hole chart 19 is fixed to the front of the detector 5 for taking a hole chart projection image. On the projection image, the geometric distortion table is prepared. The projection image obtained by measuring the object is represented by Pd(U, V, k), wherein k is a location (angle of rotation) of a scanner as mentioned above and takes a value of 1, 2, . . . , N_{k} (N_{k} is the number of all the projections), and U and V are coordinates on an original image. U is in parallel to the rotating direction of the scanner and takes a value of 1, 2, . . . , N_{m}. V is in parallel to the center axis of rotation of the scanner and takes a value of 1, 2, . . . , N_{n} (N_{m} or N_{n} is a number of matrixes of the detector. The geometric distortion corrected projection data used for reconstructing a threedimensional image is represented by P(u, v, k), wherein u and v are coordinates on the projection data. u is in parallel to the rotating direction of the scanner and takes a value of 1, 2, . . . , N_{i}. v is in parallel to the center axis of rotation of the scanner and takes a value of 1, 2, . . . , N_{j}. (N_{i} or N_{j} is a number of matrixes of the detector, and N_{m} =N_{i} and N_{n} =N_{j} are commonly held.) Between them above mentioned, the relation of P(u, v, k)=Pd(U(u, v, k), V(u, v, k), k) is held. The functions U(u, v, k) and V(u, v, k) represent the image geometric distortion. The functions are tabulated with u, v and k as indexes so that they may be used for correcting the image geometric distortion. This is the geometric distortion correction table used in the apparatus for the conebeam Xray computed tomography. Of course, the actual values of k, u, v, U and V are integers. Hence, the geometric distortion corrected projection data P(u, v, k) for all the points of (u, v, k) is derived according to the expression (7) of the fourpoint Lagrangian interpolation, where U and V are maximum integers that do not surpass U(u, v, k) and V(u, v, k). This is an expansion of the geometric distortion correction mentioned as the prior art about a projection angle (corresponding to an index) k ##EQU7##
Next, the description will be oriented to the method for preparing a geometric distortion correction table according to an embodiment of the present invention. FIG. 8 shows the unit 18 for preparing a geometric distortion correction table in detail. At first, a geometric distortion correction table generator 21 operates to apply the conventional method for generating the geometric distortion correction table to all the projections and generates the geometric distortion correction table from the hole chart projection image. As is apparent from the foregoing cause, the geometric distortion correction table does not necessarily have exact data about all the projections. Then, in a unit 22 for calculating an evaluation function, the evaluation function is calculated for evaluating whether or not the correction table of each projection is exactly prepared. The resulting function value is compared with a specific threshold value in a comparing unit 23. If the former is equal to or greater than the latter, it indicates that the correction table is not exactly prepared. If the correction table is determined to be inexact, an interpolation calculation unit 24 operates to replace the inexact table with a new correction table obtained by doing the interpolating calculation using the geometric distortion correction tables. By repeating these operations, the exact geometric distortion correction tables are prepared for all the projections and then saved in a memory 20 for saving a geometric distortion correction table.
Next, the contents of the process will be described in more detail.
If the correction table is exactly prepared, it is assumed that the correction table U(u, v, k) and V(u, v, k) are continuously changed about u, v and k. If an adverse effect such as noise inhibits to prepare the exact correction table about a certain projection, U(u, v, k) and V(u, v, k) are discontinuously changed particularly in the k direction. Using this property, the kdirectional continuity of the correction table is checked for determining whether or not the correction table is exactly prepared. In this determination, the function for evaluating the correction table about a projection k is represented by val(k). After the function val(k) is calculated for all the projections, it is determined that the geometric distortion correction table is correctly prepared for a projection which does not surpass a threshold value.
The expression (8) indicates the method for calculating the evaluation function val(k). This function to be indicated below operates to evaluate the Udirectional and the Vdirectional correction tables respectively. Hence, the function is represented by valU(k) and valV(k). If k=1 or k=N_{k}, the function calculation needs a projection corresponding to k=0 or k=N_{k} +1. In actual, such a projection does not exist. In place, the projection corresponding to k=N_{k} or k=1 is used for the function calculation. This replacement is valid since the projection is returned to an original position after one turn. ##EQU8##
The expression (8) indicates the variances of the correction table values are dispersed among a subject projection k, the previous projection (k1) and the subsequent projection (k+1) are calculated and then these variances are summed up about all the pixels of the projection k. Since the correction tables are continuously changed between the projections, the functions valU(k) and valV(k) indicate a great value if a discontinuous change appears therebetween. For calculating the variances, it is possible to use four or more projections. In this method, such a great value appears in the function valU(k_{0}) (or valV(k_{0})) for the subject inexact correction table as well as the functions for the adjacent tables such as valU(k_{0} 1) and valU(k_{0} +1) (valV(k_{0} 1) and valV(k_{0} +1)). If the function valU(k)(valV(k)) surpasses a threshold value about the continuous k=k_{0}, k_{0} +1, . . . , k_{0} +n, it indicates that the exact correction table is not prepared in the projections except the first one k_{0} and the last one k_{0} +1, concretely, the projections k=k_{0} +1, . . . , k_{0} +n1. FIG. 9 is a graph showing the function valU(k) is plotted about k. This case indicates that the correction table of k=k_{0} is not exact. In any case, it is presumed that the number of projections determined to be inexact by the foregoing checking method is far smaller than the number of all the projections. Hence, an operator may individually check the correction tables automatically determined to be inexact and determine if the correction tables are exact by himself.
Next, based on the foregoing check, the correction table determined to be inexact are removed and then replaced with a new table generated by interpolating the previous and the subsequent correction tables. For example, it is assumed that the correction table at the projection k_{0} is determined to be inexact and the exact correction tables located most closely in the k direction are those at the projections k_{1} and k_{2} (k_{1} <k_{0} <k_{2}). The correction table U (u, v, k_{0}) and V(u, v, k_{0}) at the projection k_{0} is derived by the following expression (9). ##EQU9##
For interpolating the correction tables, a higher degree interpolating operation may be used. Since, in essence, the correction table is continuously changed in the projecting direction as mentioned above, the generation of the correction table through the effect of the interpolation is quite valid. According to the foregoing embodiment, therefore, a simple method allows the exact geometric distortion correction tables to be prepared for all the projection angles without having to redesign all or part of the apparatus.
The expression (8) for calculating the evaluation function valU(k) and valV(k) is generalized by the expression (10) for calculating the evaluation function using p projections. In the case of ##EQU10##
In the case of p=1 at the expression (10), the expression (8) is obtained. If the evaluation function (the expression (10)) of the projections in the serial direction (k=k_{0}, k_{0} +1, . . . , k_{0} +n; k is integer) has a greater than a specific threshold value, the prepared correction table of the projection k=k_{0} +1, . . . , k_{0} +n1 is deleted from the memory for saving (memorizing) the correction table, the correction table of the projection in k=k_{0} +1, . . . , k_{0} +n1 is derived by interpolations of prepared correction table in k_{0} direction and (k_{0} +n) direction and then the correction table calculated by the interpolation is saved (memorized) in the memory. The interpolations are executed for deriving the correction table U(u, v, k) and V(u, v, k) which are deleted from the memory (where k=k_{0} +1, . . . , k_{0} +n1, k_{1} <k_{0} +1, k_{0} +n1<k_{2} ; k, k_{1}, k_{2} are integer). The interpolations are calculated according to the expression of; ##EQU11##
The prepared correction table U(u, v, k_{1}), V(u, v, k_{1}) is located immediately before the deleted correction table, and the prepared correction table U(u, v, k_{2}), and V(u, v, k_{2}) is located immediately after the deleted correction table.
Claims (30)
Priority Applications (4)
Application Number  Priority Date  Filing Date  Title 

JP7010322  19950126  
JP1032295  19950126  
JP15569495  19950622  
JP7155694  19950622 
Publications (1)
Publication Number  Publication Date 

US5640436A true US5640436A (en)  19970617 
Family
ID=26345574
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

US08/588,918 Expired  Lifetime US5640436A (en)  19950126  19960119  Method and apparatus for Xray computed tomography 
Country Status (1)
Country  Link 

US (1)  US5640436A (en) 
Cited By (51)
Publication number  Priority date  Publication date  Assignee  Title 

EP0849711A2 (en) *  19961220  19980624  Siemens Corporate Research, Inc.  Method and apparatus for cone beam imaging 
US5835556A (en) *  19950523  19981110  Rogalla; Patrik  Process and device for computer tomography transillumination for treatment 
US5960055A (en) *  19971219  19990928  Siemens Corporate Research, Inc.  Fast cone beam image reconstruction using a detector weight list 
DE19854917A1 (en) *  19981127  20000608  Siemens Ag  Image reconstruction method for computer tomography apparatus by adding extrapolated measuring points so that sequence begins and ends with zero value points 
US6101236A (en) *  19981002  20000808  University Of Iowa Research Foundation  Iterative method and apparatus for xray computed tomographic fluoroscopy 
US6130958A (en) *  19961129  20001010  Imaging Diagnostic Systems, Inc.  Method for reconstructing the image of an object scanned with a laser imaging apparatus 
US6282261B1 (en) *  19960221  20010828  Lunar Corporation  Multimode xray image intensifier system 
US6292578B1 (en) *  19980218  20010918  International Business Machines Corporation  System and method for restoring, describing and graphically displaying noisecorrupted boundaries in tomography images 
WO2001093201A1 (en) *  20000530  20011206  Siemens Aktiengesellschaft  Computer tomographic reconstruction with interpolation 
US6339223B1 (en) *  19980707  20020115  Kabushiki Kaisha Toshiba  Apparatus for truncation correction of transmission CT and nuclear medical diagnostic apparatus using the same 
US6542573B2 (en)  20010404  20030401  Koninklijke Philips Electronics N.V.  Method and device for the reconstruction of threedimensional images from cone beam projection data 
US20040022348A1 (en) *  20020731  20040205  Heumann John M.  Computed tomography 
US20040066909A1 (en) *  20021004  20040408  Lonn Albert Henry Roger  Multi modality imaging methods and apparatus 
US20040073584A1 (en) *  20021004  20040415  Jiang Hsieh  Methods and apparatus for truncation compensation 
US20040125908A1 (en) *  20020723  20040701  Erdogan Cesmeli  Method and apparatus for deriving motion information from projection data 
US6810102B2 (en)  20021004  20041026  Ge Medical Systems Global Technology Company, Llc  Methods and apparatus for truncation compensation 
US20040241197A1 (en) *  20010725  20041202  Beiersdorf Ag  Cosmetic or dermatological preparations including creatinine or a derivative thereof and creatine or a derivative thereof and methods of applying the preparations to the skin 
US20050078791A1 (en) *  20031010  20050414  Jianying Li  Methods and systems for smoothing 
FR2861571A1 (en) *  20031103  20050506  Ge Med Sys Global Tech Co Llc  Medical imaging device for reconstructing tomography images, has collimator positioned between Xray source and examining zone for surrounding Xray beam and delimiting Xray beam coming from radiation sensor 
US20050107443A1 (en) *  20000707  20050519  Medicure International Inc.  Pyridoxine and pyridoxal analogues: new uses 
US20050238223A1 (en) *  20040421  20051027  Jens Guhring  Flexible generation of digitally reconstructed radiographs 
US20070195923A1 (en) *  20040421  20070823  Koninklijke Philips Electronics N.V.  Cone beam ct apparatus using truncated projections and a previously acquired 3d ct image 
US20070201610A1 (en) *  20060227  20070830  Kabushiki Kaisha Toshiba  Image display apparatus and xray computed tomography apparatus 
US20080075348A1 (en) *  20060921  20080327  Dan Rappaport  Medical image analysis 
EP1970010A1 (en)  20070313  20080917  Kabushiki Kaisha Toshiba  XRay CT system, objectoutline estimating method, and image reconstructing method 
DE102008031530A1 (en)  20070709  20090115  FriedrichAlexanderUniversität ErlangenNürnberg  Method for correcting truncation artifacts 
US20090086888A1 (en) *  20071002  20090402  Akira Hagiwara  Xray ct apparatus and image reconstruction method 
US20090190714A1 (en) *  20080130  20090730  Varian Medical Systems, Inc.  Methods, Apparatus, and ComputerProgram Products for Increasing Accuracy in ConeBeam Computed Tomography 
WO2009114669A1 (en) *  20080312  20090917  Sun Nuclear Corp.  Radiation therapy plan dose perturbation system and method 
US20090274354A1 (en) *  20080430  20091105  RealTime Tomography, Llc  Dynamic tomographic image reconstruction and rendering ondemand 
US20100014778A1 (en) *  20080718  20100121  Seiko Epson Corporation  Image correcting apparatus, image correcting method, projector and projection system 
US20100172464A1 (en) *  20090107  20100708  Analogic Corporation  Method of and system for low cost implementation of dual energy ct imaging 
US7778392B1 (en) *  20041102  20100817  Pme Ip Australia Pty Ltd  Method of reconstructing computed tomography (CT) volumes suitable for execution on commodity central processing units (CPUs) and graphics processors, and apparatus operating in accord with those methods (rotational Xray on GPUs) 
US20110022360A1 (en) *  20090723  20110127  Sun Nuclear Corporation  Multiple axes scanning system and method for measuring radiation from a radiation source 
US20110188726A1 (en) *  20080618  20110804  Ram Nathaniel  Method and system for stitching multiple images into a panoramic image 
USRE43272E1 (en) *  20010403  20120327  Kabushiki Kaisha Toshiba  Xray computerized tomographic apparatus 
US8775510B2 (en)  20070827  20140708  Pme Ip Australia Pty Ltd  Fast file server methods and system 
US8976190B1 (en)  20130315  20150310  Pme Ip Australia Pty Ltd  Method and system for rule based display of sets of images 
US9019287B2 (en)  20071123  20150428  Pme Ip Australia Pty Ltd  Clientserver visualization system with hybrid data processing 
US20150254817A1 (en) *  20140304  20150910  General Electric Company  Method and system for dimensional analysis of an object 
US9355616B2 (en)  20071123  20160531  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US9433390B2 (en)  20070621  20160906  Surgix Ltd.  System for measuring the true dimensions and orientation of objects in a two dimensional image 
US9454813B2 (en)  20071123  20160927  PME IP Pty Ltd  Image segmentation assignment of a volume by comparing and correlating slice histograms with an anatomic atlas of average histograms 
US9509802B1 (en)  20130315  20161129  PME IP Pty Ltd  Method and system FPOR transferring data to improve responsiveness when sending large data sets 
US20160364889A1 (en) *  20150615  20161215  Siemens Healthcare Gmbh  Reconstruction of a resultant image taking account of contour significance data 
US9904969B1 (en)  20071123  20180227  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US9984478B2 (en)  20150728  20180529  PME IP Pty Ltd  Apparatus and method for visualizing digital breast tomosynthesis and other volumetric images 
US10070839B2 (en)  20130315  20180911  PME IP Pty Ltd  Apparatus and system for rule based visualization of digital breast tomosynthesis and other volumetric images 
US10099067B2 (en)  20141219  20181016  Sun Nuclear Corporation  Radiation therapy dose calculation 
US10311541B2 (en)  20071123  20190604  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US10413754B2 (en)  20120529  20190917  Sun Nuclear Corporation  Method and system for calorimetry probe 
Citations (1)
Publication number  Priority date  Publication date  Assignee  Title 

US5493593A (en) *  19940927  19960220  University Of Delaware  Tilted detector microscopy in computerized tomography 

1996
 19960119 US US08/588,918 patent/US5640436A/en not_active Expired  Lifetime
Patent Citations (1)
Publication number  Priority date  Publication date  Assignee  Title 

US5493593A (en) *  19940927  19960220  University Of Delaware  Tilted detector microscopy in computerized tomography 
NonPatent Citations (8)
Title 

J.Opt.Soc.Am. A/vol.1, No.6, Jun. 1984, pp.612 619. * 
J.Opt.Soc.Am. A/vol.1, No.6, Jun. 1984, pp.612619. 
JP A 5 28316(U) Japan Filing Date Sep. 27, 1991 Translation Not avail. * 
JPA528316(U) Japan Filing Date Sep. 27, 1991 Translation Not avail. 
Medical Physics, vol.6, No.5, pp.412 417, 1979. * 
Medical Physics, vol.6, No.5, pp.412417, 1979. 
SPIE Vol.2163, Physics of Medical Imaging, pp.199 210, 1994. * 
SPIE Vol.2163, Physics of Medical Imaging, pp.199210, 1994. 
Cited By (100)
Publication number  Priority date  Publication date  Assignee  Title 

US5835556A (en) *  19950523  19981110  Rogalla; Patrik  Process and device for computer tomography transillumination for treatment 
US6282261B1 (en) *  19960221  20010828  Lunar Corporation  Multimode xray image intensifier system 
US6130958A (en) *  19961129  20001010  Imaging Diagnostic Systems, Inc.  Method for reconstructing the image of an object scanned with a laser imaging apparatus 
EP0849711A3 (en) *  19961220  20000816  Siemens Corporate Research, Inc.  Method and apparatus for cone beam imaging 
EP0849711A2 (en) *  19961220  19980624  Siemens Corporate Research, Inc.  Method and apparatus for cone beam imaging 
US5960055A (en) *  19971219  19990928  Siemens Corporate Research, Inc.  Fast cone beam image reconstruction using a detector weight list 
US6292578B1 (en) *  19980218  20010918  International Business Machines Corporation  System and method for restoring, describing and graphically displaying noisecorrupted boundaries in tomography images 
US6339223B1 (en) *  19980707  20020115  Kabushiki Kaisha Toshiba  Apparatus for truncation correction of transmission CT and nuclear medical diagnostic apparatus using the same 
US6101236A (en) *  19981002  20000808  University Of Iowa Research Foundation  Iterative method and apparatus for xray computed tomographic fluoroscopy 
DE19854917B4 (en) *  19981127  20080703  Siemens Ag  Image reconstruction method for a CT device 
US6307909B1 (en)  19981127  20011023  Siemens Aktiengesellschaft  Method for image reconstruction in a computed tomography device 
DE19854917A1 (en) *  19981127  20000608  Siemens Ag  Image reconstruction method for computer tomography apparatus by adding extrapolated measuring points so that sequence begins and ends with zero value points 
WO2001093201A1 (en) *  20000530  20011206  Siemens Aktiengesellschaft  Computer tomographic reconstruction with interpolation 
US6845141B2 (en)  20000530  20050118  Siemens Aktiengesellschaft  Computed tomography apparatus wherein an image of a subject is reconstructed for a reconstruction field that is larger than the measuring field 
US20020186809A1 (en) *  20000530  20021212  Thomas Flohr  Computer tomographic reconstruction with interpolation 
US20050107443A1 (en) *  20000707  20050519  Medicure International Inc.  Pyridoxine and pyridoxal analogues: new uses 
USRE43272E1 (en) *  20010403  20120327  Kabushiki Kaisha Toshiba  Xray computerized tomographic apparatus 
US6542573B2 (en)  20010404  20030401  Koninklijke Philips Electronics N.V.  Method and device for the reconstruction of threedimensional images from cone beam projection data 
US20040241197A1 (en) *  20010725  20041202  Beiersdorf Ag  Cosmetic or dermatological preparations including creatinine or a derivative thereof and creatine or a derivative thereof and methods of applying the preparations to the skin 
US20040125908A1 (en) *  20020723  20040701  Erdogan Cesmeli  Method and apparatus for deriving motion information from projection data 
CN100375122C (en) *  20020723  20080312  通用电气公司  Method and apparatus for deriving motion information from projection data 
US6879656B2 (en) *  20020723  20050412  General Electric Company  Method and apparatus for deriving motion information from projection data 
US6765981B2 (en) *  20020731  20040720  Agilent Technologies, Inc.  Computed tomography 
US20040022348A1 (en) *  20020731  20040205  Heumann John M.  Computed tomography 
US20040066909A1 (en) *  20021004  20040408  Lonn Albert Henry Roger  Multi modality imaging methods and apparatus 
US6856666B2 (en)  20021004  20050215  Ge Medical Systems Global Technology Company, Llc  Multi modality imaging methods and apparatus 
US6810102B2 (en)  20021004  20041026  Ge Medical Systems Global Technology Company, Llc  Methods and apparatus for truncation compensation 
US20040073584A1 (en) *  20021004  20040415  Jiang Hsieh  Methods and apparatus for truncation compensation 
US7254259B2 (en)  20021004  20070807  Ge Medical Systems Global Technology Company Llc  Methods and apparatus for truncation compensation 
US6931094B2 (en)  20031010  20050816  Ge Medical Systems Global Technology Company, Llc  Methods and systems for smoothing 
US20050078791A1 (en) *  20031010  20050414  Jianying Li  Methods and systems for smoothing 
FR2861571A1 (en) *  20031103  20050506  Ge Med Sys Global Tech Co Llc  Medical imaging device for reconstructing tomography images, has collimator positioned between Xray source and examining zone for surrounding Xray beam and delimiting Xray beam coming from radiation sensor 
US7440535B2 (en) *  20040421  20081021  Koninklijke Philips Electronics N.V.  Cone beam CT apparatus using truncated projections and a previously acquired 3D CT image 
US20070195923A1 (en) *  20040421  20070823  Koninklijke Philips Electronics N.V.  Cone beam ct apparatus using truncated projections and a previously acquired 3d ct image 
US20050238223A1 (en) *  20040421  20051027  Jens Guhring  Flexible generation of digitally reconstructed radiographs 
US7653226B2 (en) *  20040421  20100126  Siemens Medical Solutions Usa, Inc.  Flexible generation of digitally reconstructed radiographs 
US7778392B1 (en) *  20041102  20100817  Pme Ip Australia Pty Ltd  Method of reconstructing computed tomography (CT) volumes suitable for execution on commodity central processing units (CPUs) and graphics processors, and apparatus operating in accord with those methods (rotational Xray on GPUs) 
US8340241B2 (en) *  20060227  20121225  Kabushiki Kaisha Toshiba  Image display apparatus and Xray computed tomography apparatus 
US20070201610A1 (en) *  20060227  20070830  Kabushiki Kaisha Toshiba  Image display apparatus and xray computed tomography apparatus 
US20080075348A1 (en) *  20060921  20080327  Dan Rappaport  Medical image analysis 
US20120183184A1 (en) *  20060921  20120719  Dan Rappaport  Medical image analysis 
US8090166B2 (en) *  20060921  20120103  Surgix Ltd.  Medical image analysis 
US9111180B2 (en) *  20060921  20150818  Orthopedic Navigation Ltd.  Medical image analysis 
US8416913B2 (en)  20070313  20130409  Kabushiki Kaisha Toshiba  Xray CT system, objectoutline estimating method, and image reconstructing method 
US20080226022A1 (en) *  20070313  20080918  Kabushiki Kaisha Toshiba  Xray ct system, objectoutline estimating method, and image reconstructing method 
EP1970010A1 (en)  20070313  20080917  Kabushiki Kaisha Toshiba  XRay CT system, objectoutline estimating method, and image reconstructing method 
US9433390B2 (en)  20070621  20160906  Surgix Ltd.  System for measuring the true dimensions and orientation of objects in a two dimensional image 
US8107708B2 (en)  20070709  20120131  Siemens Aktiengesellschaft  Method for correcting truncation artifacts 
US20090016592A1 (en) *  20070709  20090115  Stefan Hoppe  Method for correcting truncation artifacts 
DE102008031530A1 (en)  20070709  20090115  FriedrichAlexanderUniversität ErlangenNürnberg  Method for correcting truncation artifacts 
US10038739B2 (en)  20070827  20180731  PME IP Pty Ltd  Fast file server methods and systems 
US9860300B2 (en)  20070827  20180102  PME IP Pty Ltd  Fast file server methods and systems 
US9167027B2 (en)  20070827  20151020  PME IP Pty Ltd  Fast file server methods and systems 
US8775510B2 (en)  20070827  20140708  Pme Ip Australia Pty Ltd  Fast file server methods and system 
US9531789B2 (en)  20070827  20161227  PME IP Pty Ltd  Fast file server methods and systems 
US7949090B2 (en)  20071002  20110524  Ge Medical Systems Global Technology Company, Llc  Xray CT apparatus and image reconstruction method 
US20090086888A1 (en) *  20071002  20090402  Akira Hagiwara  Xray ct apparatus and image reconstruction method 
US9019287B2 (en)  20071123  20150428  Pme Ip Australia Pty Ltd  Clientserver visualization system with hybrid data processing 
US10430914B2 (en)  20071123  20191001  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US9454813B2 (en)  20071123  20160927  PME IP Pty Ltd  Image segmentation assignment of a volume by comparing and correlating slice histograms with an anatomic atlas of average histograms 
US10043482B2 (en)  20071123  20180807  PME IP Pty Ltd  Clientserver visualization system with hybrid data processing 
US9355616B2 (en)  20071123  20160531  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US9904969B1 (en)  20071123  20180227  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US10380970B2 (en)  20071123  20190813  PME IP Pty Ltd  Clientserver visualization system with hybrid data processing 
US9728165B1 (en)  20071123  20170808  PME IP Pty Ltd  Multiuser/multiGPU render server apparatus and methods 
US9984460B2 (en)  20071123  20180529  PME IP Pty Ltd  Automatic image segmentation methods and analysis 
US10311541B2 (en)  20071123  20190604  PME IP Pty Ltd  Multiuser multiGPU render server apparatus and methods 
US9595242B1 (en)  20071123  20170314  PME IP Pty Ltd  Clientserver visualization system with hybrid data processing 
US20090190714A1 (en) *  20080130  20090730  Varian Medical Systems, Inc.  Methods, Apparatus, and ComputerProgram Products for Increasing Accuracy in ConeBeam Computed Tomography 
US8320518B2 (en)  20080130  20121127  Varian Medical Systems, Inc.  Methods, apparatus, and computerprogram products for increasing accuracy in conebeam computed tomography 
US8009794B2 (en) *  20080130  20110830  Varian Medical Systems, Inc.  Methods, apparatus, and computerprogram products for increasing accuracy in conebeam computed tomography 
WO2009114669A1 (en) *  20080312  20090917  Sun Nuclear Corp.  Radiation therapy plan dose perturbation system and method 
US20090252292A1 (en) *  20080312  20091008  Sun Nucear, Corp.  Radiation therapy plan dose perturbation system and method 
US7945022B2 (en)  20080312  20110517  Sun Nuclear Corp.  Radiation therapy plan dose perturbation system and method 
US8233690B2 (en) *  20080430  20120731  RealTime Tomography, Llc  Dynamic tomographic image reconstruction and rendering ondemand 
US20090274354A1 (en) *  20080430  20091105  RealTime Tomography, Llc  Dynamic tomographic image reconstruction and rendering ondemand 
US9109998B2 (en)  20080618  20150818  Orthopedic Navigation Ltd.  Method and system for stitching multiple images into a panoramic image 
US20110188726A1 (en) *  20080618  20110804  Ram Nathaniel  Method and system for stitching multiple images into a panoramic image 
US20100014778A1 (en) *  20080718  20100121  Seiko Epson Corporation  Image correcting apparatus, image correcting method, projector and projection system 
US20100172464A1 (en) *  20090107  20100708  Analogic Corporation  Method of and system for low cost implementation of dual energy ct imaging 
US7970096B2 (en) *  20090107  20110628  Analogic Corporation  Method of and system for low cost implementation of dual energy CT imaging 
US8321179B2 (en)  20090723  20121127  Sun Nuclear Corporation  Multiple axes scanning system and method for measuring radiation from a radiation source 
US20110022360A1 (en) *  20090723  20110127  Sun Nuclear Corporation  Multiple axes scanning system and method for measuring radiation from a radiation source 
US10413754B2 (en)  20120529  20190917  Sun Nuclear Corporation  Method and system for calorimetry probe 
US10070839B2 (en)  20130315  20180911  PME IP Pty Ltd  Apparatus and system for rule based visualization of digital breast tomosynthesis and other volumetric images 
US9524577B1 (en)  20130315  20161220  PME IP Pty Ltd  Method and system for rule based display of sets of images 
US10373368B2 (en)  20130315  20190806  PME IP Pty Ltd  Method and system for rulebased display of sets of images 
US10320684B2 (en)  20130315  20190611  PME IP Pty Ltd  Method and system for transferring data to improve responsiveness when sending large data sets 
US8976190B1 (en)  20130315  20150310  Pme Ip Australia Pty Ltd  Method and system for rule based display of sets of images 
US9749245B2 (en)  20130315  20170829  PME IP Pty Ltd  Method and system for transferring data to improve responsiveness when sending large data sets 
US9898855B2 (en)  20130315  20180220  PME IP Pty Ltd  Method and system for rule based display of sets of images 
US9509802B1 (en)  20130315  20161129  PME IP Pty Ltd  Method and system FPOR transferring data to improve responsiveness when sending large data sets 
US20150254817A1 (en) *  20140304  20150910  General Electric Company  Method and system for dimensional analysis of an object 
US9351697B2 (en) *  20140304  20160531  General Electric Company  Method and system for dimensional analysis of an object 
US10099067B2 (en)  20141219  20181016  Sun Nuclear Corporation  Radiation therapy dose calculation 
CN106251295A (en) *  20150615  20161221  西门子医疗有限公司  Consider the reconstruction of the result images of profile saliency data 
US9947117B2 (en) *  20150615  20180417  Siemens Healthcare Gmbh  Reconstruction of a resultant image taking account of contour significance data 
US20160364889A1 (en) *  20150615  20161215  Siemens Healthcare Gmbh  Reconstruction of a resultant image taking account of contour significance data 
US10395398B2 (en)  20150728  20190827  PME IP Pty Ltd  Appartus and method for visualizing digital breast tomosynthesis and other volumetric images 
US9984478B2 (en)  20150728  20180529  PME IP Pty Ltd  Apparatus and method for visualizing digital breast tomosynthesis and other volumetric images 
Similar Documents
Publication  Publication Date  Title 

Hsieh et al.  A novel reconstruction algorithm to extend the CT scan field‐of‐view  
CA2060181C (en)  Method and apparatus for computing tomographic scans  
CA1124890A (en)  Correction for polychromatic aberration in ct images  
US5909476A (en)  Iterative process for reconstructing conebeam tomographic images  
US4969110A (en)  Method of using a priori information in computerized tomography  
US5270923A (en)  Computed tomographic image reconstruction method for helical scanning using interpolation of partial scans for image construction  
JP4920205B2 (en)  System and method for boundary estimation using CT measurements  
JP3843169B2 (en)  Method for correcting defects in radiographic image on recording member  
US7382852B2 (en)  Method and apparatus for correcting motion in image reconstruction  
US5390112A (en)  Threedimensional computerized tomography scanning method and system for imaging large objects with smaller area detectors  
Horn  Density reconstruction using arbitrary raysampling schemes  
US6690841B2 (en)  Method and apparatus for image registration  
US4761819A (en)  Adaptive noise reduction filter for reconstructed images  
US5406479A (en)  Method for rebinning and for correcting cone beam error in a fan beam computed tomographic scanner system  
US7415145B2 (en)  Methods and apparatus for artifact reduction  
US6035012A (en)  Artifact correction for highly attenuating objects  
US4550371A (en)  Method and apparatus for compensating CT images for truncated projections  
JP3761094B2 (en)  Method for reconstructing a threedimensional image of an object  
US5485494A (en)  Modulation of Xray tube current during CT scanning  
EP1048008B1 (en)  Methods and apparatus for calcification scoring  
US5046003A (en)  Method for reducing skew image artifacts in helical projection imaging  
US5546472A (en)  Feature guided method and apparatus for obtaining an image of an object  
JP5737725B2 (en)  Localization and highly limited image reconstruction methods  
US6848087B2 (en)  Subresolution alignment of images  
US6768782B1 (en)  Iterative method for regionofinterest reconstruction 
Legal Events
Date  Code  Title  Description 

AS  Assignment 
Owner name: HITACHI MEDICAL CORPORATION, JAPAN Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KAWAI, HIROYUKI;SEKIHARA, KENSUKE;REEL/FRAME:007844/0836 Effective date: 19960109 

STCF  Information on status: patent grant 
Free format text: PATENTED CASE 

FPAY  Fee payment 
Year of fee payment: 4 

FPAY  Fee payment 
Year of fee payment: 8 

FPAY  Fee payment 
Year of fee payment: 12 